


import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__ ,   __magic_name__=0      ):
 # Format the message.
 if name is None:
  _lowercase:       Optional[int]						=				None
 else:
  _lowercase:       str						=				'.' * max(0 ,   spaces - 2      ) + '# {:' + str(5_0 - spaces      ) + 's}'
  _lowercase:       Any						=				fmt.format(_A      )

 # Print and recurse (if needed).
 if isinstance(_A ,   _A      ):
  if msg is not None:
   print(_A      )
  for k in val.keys():
   recursive_print(_A ,   val[k] ,   spaces + 2      )
 elif isinstance(_A ,   torch.Tensor      ):
  print(_A ,   ":" ,   val.size()      )
 else:
  print(_A ,   ":" ,   _A      )


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__ ,   __magic_name__ ,   __magic_name__ ,   __magic_name__      ):
 # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
 # for compatibility with later versions of NVIDIA Megatron-LM.
 # The inverse operation is performed inside Megatron-LM to read checkpoints:
 # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
 # If param is the weight tensor of the self-attention block, the returned tensor
 # will have to be transposed one more time to be read by HuggingFace GPT2.
 _lowercase:       List[str]						=				param.size()
 if checkpoint_version == 1.0:
  # version 1.0 stores [num_heads * hidden_size * num_splits, :]
  _lowercase:       int						=				(num_heads, hidden_size, num_splits) + input_shape[1:]
  _lowercase:       List[str]						=				param.view(*_A      )
  _lowercase:       Optional[int]						=				param.transpose(0 ,   2      )
  _lowercase:       Union[str, Any]						=				param.transpose(1 ,   2      ).contiguous()
 elif checkpoint_version >= 2.0:
  # other versions store [num_heads * num_splits * hidden_size, :]
  _lowercase:       str						=				(num_heads, num_splits, hidden_size) + input_shape[1:]
  _lowercase:       Any						=				param.view(*_A      )
  _lowercase:       Optional[Any]						=				param.transpose(0 ,   1      ).contiguous()
 _lowercase:       Tuple						=				param.view(*_A      )
 return param


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__ ,   __magic_name__      ):
 # The converted output model.
 _lowercase:       Dict						=				{}

 # old versions did not store training args
 _lowercase:       Optional[int]						=				input_state_dict.get("args" ,   _A      )
 if ds_args is not None:
  # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
  # from pprint import pprint
  # pprint(vars(ds_args))

  _lowercase:       List[str]						=				ds_args.padded_vocab_size
  _lowercase:       Optional[Any]						=				ds_args.max_position_embeddings
  _lowercase:       Optional[Any]						=				ds_args.hidden_size
  _lowercase:       Any						=				ds_args.num_layers
  _lowercase:       str						=				ds_args.num_attention_heads
  _lowercase:       Dict						=				ds_args.ffn_hidden_size
  # pprint(config)

 # The number of heads.
 _lowercase:       Dict						=				config.n_head
 # The hidden_size per head.
 _lowercase:       Dict						=				config.n_embd // config.n_head
 # Megatron-LM checkpoint version
 if "checkpoint_version" in input_state_dict.keys():
  _lowercase:       int						=				input_state_dict['checkpoint_version']
 else:
  _lowercase:       int						=				0.0

 # The model.
 _lowercase:       Optional[Any]						=				input_state_dict['model']
 # The language model.
 _lowercase:       Any						=				model['language_model']
 # The embeddings.
 _lowercase:       List[str]						=				lm['embedding']

 # The word embeddings.
 _lowercase:       List[str]						=				embeddings['word_embeddings']['weight']
 # Truncate the embedding table to vocab_size rows.
 _lowercase:       List[Any]						=				word_embeddings[: config.vocab_size, :]
 _lowercase:       Optional[int]						=				word_embeddings

 # The position embeddings.
 _lowercase:       Any						=				embeddings['position_embeddings']['weight']
 # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
 _lowercase:       Union[str, Any]						=				pos_embeddings.size(0      )
 if n_positions != config.n_positions:
  raise ValueError(
      f"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"      )
 # Store the position embeddings.
 _lowercase:       Union[str, Any]						=				pos_embeddings

 # The transformer.
 _lowercase:       Optional[int]						=				lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

 # The regex to extract layer names.
 _lowercase:       Optional[Any]						=				re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"      )

 # The simple map of names for "automated" rules.
 _lowercase:       str						=				{
     'attention.dense': '.attn.c_proj.',
     'self_attention.dense': '.attn.c_proj.',
     'mlp.dense_h_to_4h': '.mlp.c_fc.',
     'mlp.dense_4h_to_h': '.mlp.c_proj.',
 }

 # Extract the layers.
 for key, val in transformer.items():
  # Match the name.
  _lowercase:       str						=				layer_re.match(_A      )

  # Stop if that's not a layer
  if m is None:
   break

  # The index of the layer.
  _lowercase:       Union[str, Any]						=				int(m.group(1      )      )
  # The name of the operation.
  _lowercase:       str						=				m.group(2      )
  # Is it a weight or a bias?
  _lowercase:       List[str]						=				m.group(3      )

  # The name of the layer.
  _lowercase:       Dict						=				f"transformer.h.{layer_idx}"

  # For layernorm(s), simply store the layer norm.
  if op_name.endswith("layernorm"      ):
   _lowercase:       int						=				'ln_1' if op_name.startswith("input"      ) else 'ln_2'
   _lowercase:       Optional[int]						=				val

  # Transpose the QKV matrix.
  elif (
      op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
  ) and weight_or_bias == "weight":
   # Insert a tensor of 1x1xDxD bias.
   _lowercase:       Optional[Any]						=				torch.tril(torch.ones((n_positions, n_positions) ,   dtype=torch.floataa      )      ).view(
       1 ,   1 ,   _A ,   _A      )
   _lowercase:       List[Any]						=				causal_mask

   # Insert a "dummy" tensor for masked_bias.
   _lowercase:       Optional[Any]						=				torch.tensor(-1e4 ,   dtype=torch.floataa      )
   _lowercase:       Dict						=				masked_bias

   _lowercase:       Dict						=				fix_query_key_value_ordering(_A ,   _A ,   3 ,   _A ,   _A      )
   # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
   _lowercase:       Any						=				out_val.transpose(0 ,   1      ).contiguous()
   # Store.
   _lowercase:       Optional[int]						=				out_val

  # Transpose the bias.
  elif (
      op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
  ) and weight_or_bias == "bias":
   _lowercase:       List[str]						=				fix_query_key_value_ordering(_A ,   _A ,   3 ,   _A ,   _A      )
   # Store. No change of shape.
   _lowercase:       List[str]						=				out_val

  # Transpose the weights.
  elif weight_or_bias == "weight":
   _lowercase:       Tuple						=				megatron_to_transformers[op_name]
   _lowercase:       Optional[int]						=				val.transpose(0 ,   1      )

  # Copy the bias.
  elif weight_or_bias == "bias":
   _lowercase:       Dict						=				megatron_to_transformers[op_name]
   _lowercase:       int						=				val

  # DEBUG.
 assert config.n_layer == layer_idx + 1

 # The final layernorm.
 _lowercase:       Any						=				transformer['final_layernorm.weight']
 _lowercase:       Optional[Any]						=				transformer['final_layernorm.bias']

 # For LM head, transformers' wants the matrix to weight embeddings.
 _lowercase:       str						=				word_embeddings

 # It should be done!
 return output_state_dict


def 							__lowerCAmelCase      (			):
 # Create the argument parser.
 _lowercase:       str						=				argparse.ArgumentParser()
 parser.add_argument("--print-checkpoint-structure" ,   action="store_true"      )
 parser.add_argument(
     "path_to_checkpoint" ,   type=_A ,   help="Path to the checkpoint file (.zip archive or direct .pt file)" ,   )
 parser.add_argument(
     "--config_file" ,   default="" ,   type=_A ,   help="An optional config json file describing the pre-trained model." ,   )
 _lowercase:       str						=				parser.parse_args()

 # Extract the basename.
 _lowercase:       str						=				os.path.dirname(args.path_to_checkpoint      )

 # Load the model.
 # the .zip is very optional, let's keep it for backward compatibility
 print(f"Extracting PyTorch state dictionary from {args.path_to_checkpoint}"      )
 if args.path_to_checkpoint.endswith(".zip"      ):
  with zipfile.ZipFile(args.path_to_checkpoint ,   "r"      ) as checkpoint:
   with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"      ) as pytorch_dict:
    _lowercase:       int						=				torch.load(_A ,   map_location="cpu"      )
 else:
  _lowercase:       Dict						=				torch.load(args.path_to_checkpoint ,   map_location="cpu"      )

 _lowercase:       Optional[Any]						=				input_state_dict.get("args" ,   _A      )

 # Read the config, or default to the model released by NVIDIA.
 if args.config_file == "":
  if ds_args is not None:
   if ds_args.bias_gelu_fusion:
    _lowercase:       List[Any]						=				'gelu_fast'
   elif ds_args.openai_gelu:
    _lowercase:       str						=				'gelu_new'
   else:
    _lowercase:       List[Any]						=				'gelu'
  else:
   # in the very early days this used to be "gelu_new"
   _lowercase:       str						=				'gelu_new'

  # Spell out all parameters in case the defaults change.
  _lowercase:       List[str]						=				GPTaConfig(
      vocab_size=5_0_2_5_7 ,   n_positions=1_0_2_4 ,   n_embd=1_0_2_4 ,   n_layer=2_4 ,   n_head=1_6 ,   n_inner=4_0_9_6 ,   activation_function=_A ,   resid_pdrop=0.1 ,   embd_pdrop=0.1 ,   attn_pdrop=0.1 ,   layer_norm_epsilon=1e-5 ,   initializer_range=0.02 ,   summary_type="cls_index" ,   summary_use_proj=_A ,   summary_activation=_A ,   summary_proj_to_labels=_A ,   summary_first_dropout=0.1 ,   scale_attn_weights=_A ,   use_cache=_A ,   bos_token_id=5_0_2_5_6 ,   eos_token_id=5_0_2_5_6 ,   )
 else:
  _lowercase:       Optional[int]						=				GPTaConfig.from_json_file(args.config_file      )

 _lowercase:       Union[str, Any]						=				['GPT2LMHeadModel']

 # Convert.
 print("Converting"      )
 _lowercase:       List[Any]						=				convert_megatron_checkpoint(_A ,   _A ,   _A      )

 # Print the structure of converted state dict.
 if args.print_checkpoint_structure:
  recursive_print(_A ,   _A      )

 # Add tokenizer class info to config
 # see https://github.com/huggingface/transformers/issues/13906)
 if ds_args is not None:
  _lowercase:       Union[str, Any]						=				ds_args.tokenizer_type
  if tokenizer_type == "GPT2BPETokenizer":
   _lowercase:       Dict						=				'gpt2'
  elif tokenizer_type == "PretrainedFromHF":
   _lowercase:       Optional[int]						=				ds_args.tokenizer_name_or_path
  else:
   raise ValueError(f"Unrecognized tokenizer_type {tokenizer_type}"      )
 else:
  _lowercase:       int						=				'gpt2'

 _lowercase:       Tuple						=				AutoTokenizer.from_pretrained(_A      )
 _lowercase:       Any						=				type(_A      ).__name__
 _lowercase:       int						=				tokenizer_class

 # Store the config to file.
 print("Saving config"      )
 config.save_pretrained(_A      )

 # Save tokenizer based on args
 print(f"Adding {tokenizer_class} tokenizer files"      )
 tokenizer.save_pretrained(_A      )

 # Store the state_dict to file.
 _lowercase:       Any						=				os.path.join(_A ,   "pytorch_model.bin"      )
 print(f"Saving checkpoint to \"{output_checkpoint_file}\""      )
 torch.save(_A ,   _A      )


####################################################################################################

if __name__ == "__main__":
       main()

####################################################################################################






'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   Tuple					=		logging.get_logger(__name__)

lowerCAmelCase:   Any					=		{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """pegasus"""
    lowercase__					=       ["""past_key_values"""]
    lowercase__					=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







    def __init__( self					:    Union[str, Any]		,     __snake_case					:    Tuple=5_02_65		,     __snake_case					:    List[Any]=10_24		,     __snake_case					:    Optional[Any]=12		,     __snake_case					:    Tuple=40_96		,     __snake_case					:    str=16		,     __snake_case					:    int=12		,     __snake_case					:    Tuple=40_96		,     __snake_case					:    int=16		,     __snake_case					:    Tuple=0.0		,     __snake_case					:    Any=0.0		,     __snake_case					:    Any=True		,     __snake_case					:    Optional[int]=True		,     __snake_case					:    Any="gelu"		,     __snake_case					:    str=10_24		,     __snake_case					:    Dict=0.1		,     __snake_case					:    List[Any]=0.0		,     __snake_case					:    str=0.0		,     __snake_case					:    str=0.02		,     __snake_case					:    Union[str, Any]=0		,     __snake_case					:    List[Any]=False		,     __snake_case					:    Optional[Any]=0		,     __snake_case					:    str=1		,     __snake_case					:    Any=1		,     **__snake_case					:    int		,     ):
      a		:   Optional[int]	  =	vocab_size
      a		:   List[str]	  =	max_position_embeddings
      a		:   List[Any]	  =	d_model
      a		:   Tuple	  =	encoder_ffn_dim
      a		:   List[str]	  =	encoder_layers
      a		:   str	  =	encoder_attention_heads
      a		:   str	  =	decoder_ffn_dim
      a		:   Optional[int]	  =	decoder_layers
      a		:   Optional[int]	  =	decoder_attention_heads
      a		:   Optional[int]	  =	dropout
      a		:   Any	  =	attention_dropout
      a		:   str	  =	activation_dropout
      a		:   List[Any]	  =	activation_function
      a		:   Union[str, Any]	  =	init_std
      a		:   List[Any]	  =	encoder_layerdrop
      a		:   Union[str, Any]	  =	decoder_layerdrop
      a		:   List[Any]	  =	use_cache
      a		:   Any	  =	encoder_layers
      a		:   Optional[int]	  =	scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=__snake_case		,     eos_token_id=__snake_case		,     is_encoder_decoder=__snake_case		,     decoder_start_token_id=__snake_case		,     forced_eos_token_id=__snake_case		,     **__snake_case		,     )







    @property
    def      lowercase_			( self					:    int      ):
      return self.encoder_attention_heads







    @property
    def      lowercase_			( self					:    int      ):
      return self.d_model